FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition

• URL: http://arxiv.org/abs/2007.07296v5
• Date: Sat, 6 May 2023 16:45:17 GMT
• Title: FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition
• Authors: Hanchi Ren, Jingjing Deng, Xianghua Xie, Xiaoke Ma and Yichuan Wang
• Abstract summary: Federated Learning (FL) framework allows learning a shared model collaboratively without data being centralized or shared among data owners. We show in this paper that the generalization ability of the joint model is poor on Non-Independent and Non-Identically Distributed (Non-IID) data. We propose a novel boosting algorithm for FL to address both the generalization and gradient leakage issues.
• Score: 7.988454173034258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, privacy and gradient protection. The recently proposed Federated Learning (FL) framework allows learning a shared model collaboratively without data being centralized or shared among data owners. However, we show in this paper that the generalization ability of the joint model is poor on Non-Independent and Non-Identically Distributed (Non-IID) data, particularly when the Federated Averaging (FedAvg) strategy is used due to the weight divergence phenomenon. Hence, we propose a novel boosting algorithm for FL to address both the generalization and gradient leakage issues, as well as achieve faster convergence in gradient-based optimization. In addition, a secure gradient sharing protocol using Homomorphic Encryption (HE) and Differential Privacy (DP) is introduced to defend against gradient leakage attack and avoid pairwise encryption that is not scalable. We demonstrate the proposed Federated Boosting (FedBoosting) method achieves noticeable improvements in both prediction accuracy and run-time efficiency in a visual text recognition task on public benchmark.

Related papers

• Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting [11.185176107646956]
  Power system load data can inadvertently reveal the daily routines of residential users, posing a risk to their property security. We introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm.
  arXiv  Detail & Related papers  (2024-02-02T16:39:08Z)
• Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity [60.791736094073]
  Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. We propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of SIGNSGD. The proposed scheme is validated through experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets.
  arXiv  Detail & Related papers  (2023-02-19T17:42:35Z)
• OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization [26.386265547513887]
  Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without sharing the raw data. Recent works show that it's still not sufficient to prevent privacy leakage from the training process or the trained model. This paper focuses on studying the privacy-preserving tree boosting algorithms under the vertical FL.
  arXiv  Detail & Related papers  (2022-10-04T02:21:18Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)
• sqSGD: Locally Private and Communication Efficient Federated Learning [14.60645909629309]
  Federated learning (FL) is a technique that trains machine learning models from decentralized data sources. We develop a gradient-based learning algorithm called sqSGD that addresses communication efficiency and high-dimensional compatibility. Experiment results show sqSGD successfully learns large models like LeNet and ResNet with local privacy constraints.
  arXiv  Detail & Related papers  (2022-06-21T17:45:35Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Do Gradient Inversion Attacks Make Federated Learning Unsafe? [70.0231254112197]
  Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. Recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in real FL use-cases and provide a new baseline attack.
  arXiv  Detail & Related papers  (2022-02-14T18:33:12Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model [8.87104231451079]
  This paper presents the first gradient-free federated learning framework called GRAFFL. It uses implicit information derived from each participating institution to learn posterior distributions of parameters. We propose the GRAFFL-based Bayesian mixture model to serve as a proof-of-concept of the framework.
  arXiv  Detail & Related papers  (2020-08-29T07:19:44Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.